Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis  

作　　者：Fanghui Bi Xin Luo Bo Shen Hongli Dong Zidong Wang 

机构地区：[1]the Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and also with the Chongqing School,University of Chinese Academy of Sciences,Chongqing 400714,China [2]the College of Computer and Information Science,Southwest University,Chongqing 400715,China [3]the College of Information Science and Technology,Donghua University,Shanghai 201620,China [4]the Artificial Intelligence Energy Research Institute,Northeast Petroleum University,Daqing 163318,China [5]the Department of Computer Science,Brunel University London,Uxbridge UB83PH,United Kingdom

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第6期1388-1406,共19页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62272078,U21A2019);the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105);the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2021-035A)。

摘　　要：High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor analysis(NLFA)model can perform representation learning to HDI data efficiently.However,existing NLFA models suffer from either slow convergence rate or representation accuracy loss.To address this issue,this paper proposes a proximal alternating-directionmethod-of-multipliers-based nonnegative latent factor analysis(PAN)model with two-fold ideas:(1)adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency;and(2)incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data.Theoretical studies verify that PAN converges to a Karush-KuhnTucker(KKT)stationary point of its nonnegativity-constrained learning objective with its learning scheme.Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.

关 键 词：NONNEGATIVE REPRESENTATION CONVERGENCE 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]